Rainfall station spatial distribution assessment method

Abstract

The invention relates to the technical field of rainfall stations, in particular to a rainfall station spatial distribution evaluation method and a medium. Comprising the steps of calculating a current information amount score of a target site; calculating a time-varying score of the target station; calculating a coverage area score of the target site; calculating a weight; and weighting the current information content score, the time-varying score and the coverage area score, and calculating a comprehensive score of the target station. According to the method, multiple scoring modes with basically different purposes are provided, multi-target fusion evaluation can be completed based on different evaluation standards, and the method is suitable for any rainfall station optimization scene.

Description

Technical Field

[0001] The invention relates to the technical field of rain gauge stations, and in particular to a method and medium for evaluating the spatial distribution of rain gauge stations. Background Art

[0002] Rain gauges are the most accurate measurement tools for rainfall data. In today's era of big data serving meteorology, various prediction models require accurate data from rain gauges. Therefore, the distribution requirements for rain gauges are becoming increasingly higher. The more data from rain gauges, the more accurate the data obtained when using the interpolation method. Of course, the installation and maintenance costs will also increase accordingly.

[0003] The rationality of the distribution of the rain gauge network is closely related to the real-time layout strategy. For example, when the strategy aims to maximize the efficiency of the least number of stations, the number of rain gauge stations per unit area needs to be used as the main evaluation factor; when the strategy aims to improve the interpolation accuracy, the amount of information between adjacent stations needs to be used as the evaluation factor. In addition, factors such as the maintenance optimization time cycle and the maintenance budget need to be considered. This has led to the constant changes in the evaluation methods of rain gauges. At present, there is no scientific, reasonable and long-term evaluation method that can take into account all aspects. Summary of the invention

[0004] The present invention discloses a method for evaluating the spatial distribution of rainfall stations, and the specific method is as follows:

[0005] Obtain the current rainfall data measured at the target site and several adjacent sites, and calculate the current information score of the target site using the information entropy of the target site;

[0006] Retrieve the historical information volume score of the target site, and calculate the time-varying score of the target site according to the current information volume score of the target site and the change in the existing information volume score;

[0007] Obtain estimated rainfall data calculated by remote sensing satellites, circle adjacent spaces whose difference from the estimated rainfall data of the target site is within a first preset value range, and calculate the coverage area score of the target site according to the area of ​​the adjacent spaces and the position distribution of the target site in the adjacent spaces;

[0008] The current information score, time-varying score, and coverage area score are weighted to calculate the comprehensive score of the target site.

[0009] The advantage of this embodiment is that different evaluation indicators are provided based on different evaluation criteria. In combination with actual conditions, multi-objective fusion evaluation can be completed by adjusting the weights of different scoring indicators. It is suitable for any rainfall station optimization scenario.

[0010] Furthermore, when the comprehensive scores of several rainfall stations in the region are lower than the warning value, regional optimization of the rainfall station network is started. The specific method is as follows:

[0011] Taking the comprehensive score as the optimization goal, the optimization algorithm is used to adjust the position of each rain gauge in the rain gauge network so that the sum of the comprehensive scores of each rain gauge in the rain gauge network is maximized.

[0012] Furthermore, the optimization algorithm is a genetic algorithm, and the specific method is as follows:

[0013] The spatial coordinate distribution of the current rainfall stations in the rainfall station network is used as the initialization population;

[0014] Calculate the comprehensive score and of the initialized population;

[0015] The population is selected, crossed, and mutated to generate several new spatial distributions of rainfall stations;

[0016] Calculate the comprehensive score sum of the spatial distribution of new rainfall stations to form the offspring population;

[0017] Merge the parent and offspring populations to select the next generation input population;

[0018] Repeat the selection, crossover, mutation, calculation and screening operations until the convergence condition is met;

[0019] The spatial distribution of several new rain gauges at the final convergence is taken as the optimization result of the rain gauge network.

[0020] The advantage of this embodiment is that the comprehensive scoring can reflect whether the rainfall station network meets the current actual needs and provide a scoring basis for optimizing the rainfall station network.

[0021] Furthermore, the current information volume score of the target site is calculated as follows:

[0022] Preset a number of data intervals and assign interval numbers to each interval;

[0023] The current rainfall data measured at the target station and several adjacent stations are divided into corresponding intervals, and an array is constructed with interval numbers;

[0024] Calculate the information entropy of the rainfall interval of the target station;

[0025] 1 minus the absolute value of the difference between the information entropy of the target station rainfall interval and the preset standard information entropy divided by the quotient of the preset standard information entropy, and then multiplied by 100 to calculate the current information score.

[0026] The advantage of this embodiment is that the rainfall measured by a reasonably designed rain gauge should have a reasonable information entropy; if the information entropy is too high, it means that the information measured by the target site and the adjacent sites is highly disordered, and the accuracy is difficult to guarantee when the interpolation method is used later; if the information entropy is too low, it means that the similarity between the information measured by the target site and the adjacent sites is too high, the information value is low, and the information cost is increased; therefore, by presetting the standard information entropy, it is possible to evaluate whether the information measured by the target site is reasonable, and the higher the information score, the more valuable the rainfall measured at the target site.

[0027] Furthermore, the time-varying score of the target site is calculated as follows:

[0028] Obtain the information score of several consecutive rainfalls at the target site;

[0029] Calculate the standard deviation of the array composed of several rainfall information scores;

[0030] 1 minus the standard deviation divided by the preset maximum standard deviation, then multiplied by 100, is used to calculate the time-varying score of the current target site.

[0031] The advantage of this embodiment is that as the climate changes, the information value of the rainfall measured at the same target site will also change. In particular, for target sites that need to be deployed locally for a long time, it is necessary to quantify the value of their long-term deployment. The higher the time-varying score of the target site, the smaller the deviation of the information value of the target site.

[0032] Furthermore, the coverage area score of the target site is calculated as follows:

[0033] Estimate the area of ​​adjacent space based on estimated rainfall data calculated by remote sensing satellites;

[0034] The number of target sites and several adjacent sites is divided by the area of ​​the adjacent space to calculate the number of sites per unit area.

[0035] The coverage area score of the target site is calculated by dividing the current number of sites per unit area by the preset maximum number of sites per unit area and multiplying the result by 100.

[0036] The advantage of this embodiment is that the coverage area score can evaluate the cost-effectiveness of the target site setting. The larger the coverage area, the lower the setting cost, which can balance the cost increase caused by the information volume score.

[0037] Furthermore, the current information score, time-varying score and coverage score are weighted. The specific method is as follows:

[0038] The product of the current first weight and the current information score, plus the product of the current second weight and the time-varying score, plus the product of the current third weight and the coverage area score.

[0039] Furthermore, the calculation method of the current first weight, the current second weight and the current third weight is as follows:

[0040] Initialize the first weight, the second weight, and the third weight;

[0041] Calculate the difference between the information entropy score of the target site and the average information entropy score of the adjacent sites. When the absolute value of the difference increases, reduce the historical first weight, otherwise increase the historical first weight.

[0042] Obtain the optimization adjustment plan of the rainfall station network. When the optimization adjustment plan cycle exceeds the average cycle, the historical second weight is increased, otherwise the historical second weight is reduced;

[0043] Obtain the construction budget of the rain gauge station. When the construction budget exceeds the historical average cost, the historical third weight will be reduced, otherwise it will be increased.

[0044] The normalized and corrected historical first weight, historical second weight and historical third weight are used to calculate the current first weight, current second weight and current third weight.

[0045] The advantage of this embodiment is that it provides a method for fusing decisions through weights and provides a method for quantitatively adjusting weight settings for different objectives, making the fusion evaluation truly executable and applicable to the evaluation of any rainfall station.

[0046] The present invention also discloses a storage medium storing a plurality of instructions, and a processor loads the instructions to execute the above-mentioned rainfall station spatial distribution assessment method.

[0047] Similarly, the present invention also discloses a storage medium, wherein the storage medium stores a plurality of instructions, wherein the instructions are suitable for loading by a processor, in accordance with the above method.

[0048] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings of the present invention are as follows.

[0050] Figure 1 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0052] A method for assessing the spatial distribution of rainfall stations, such as Figure 1 As shown, the specific steps are as follows:

[0053] S1. Calculate the current information volume score of the target site. The specific steps are as follows:

[0054] S11. Obtain current rainfall data of the target site and current rainfall data measured at several adjacent stations In this embodiment, the number of adjacent sites is 8, and k is the number of current rainfall data.

[0055] S12, preset several data intervals th1, th2, ..., th l , th l is the lth data interval point, and each interval is numbered th 1,2 ,th 2,3 ,th l,l-1 .

[0056] S13. By comparing the numerical values ​​of the site rainfall and the interval, the current rainfall data measured at the target site and several adjacent sites are divided into corresponding intervals, and an array {IN1, IN2, IN3, ..., IN9} is constructed with interval numbers, where IN1 is the data interval where the target site rainfall data is located, and IN2 to IN9 are the data intervals where the rainfall data of the eight adjacent sites are located.

[0057] S14. Calculate the information entropy of the interval where the target station rainfall is located. The formula is as follows:

[0058]

[0059] S15, 1 minus, the information entropy of the target station rainfall interval minus the preset standard information entropy H standard The absolute value of the difference is divided by the quotient of the preset standard information entropy, and then multiplied by 100 to calculate the current information volume score. The calculation formula is as follows:

[0060]

[0061] S2. Calculate the time-varying score of the target site. The specific steps are as follows:

[0062] S21, obtain the information score of several consecutive rainfalls at the target site {score1, score2, ..., score k};

[0063] S22. Calculate the standard deviation of the array composed of several rainfall information scores. The specific formula is as follows:

[0064]

[0065] In the formula, The average of the ratings array.

[0066] S23, 1 minus the standard deviation divided by the preset maximum standard deviation, and then multiplied by 100 to calculate the time-varying score of the current target site. The calculation formula is as follows:

[0067]

[0068] In the formula, s max is the preset maximum standard deviation.

[0069] S3. Calculate the coverage area score of the target site. The specific steps are as follows:

[0070] S31. Estimate the area of ​​the adjacent space based on the estimated rainfall data calculated by the remote sensing satellite.

[0071] S32. Divide the number of target sites and several adjacent sites by the area of ​​the adjacent space to calculate the number of sites per unit area, that is,

[0072] S33, dividing the current number of sites per unit area by the preset maximum number of sites per unit area, and multiplying by 100, to calculate the coverage area score of the target site, the calculation formula is as follows:

[0073]

[0074] Where, d max The preset maximum number of sites per unit area.

[0075] S4. Calculate the weight. The specific steps are as follows:

[0076] S41, initializing a first weight w1, a second weight w2, and a third weight w3;

[0077] S42, calculating the difference between the information entropy score of the target site and the average information entropy score of the adjacent sites, and when the absolute value of the difference increases, reducing the historical first weight, otherwise increasing the historical first weight:

[0078] When it increases, it means that the amount of rainfall information measured at the target station is too much or too little compared to the adjacent stations, which reduces or increases w1, thereby reducing the comprehensive score. The specific reduction value can be adjusted and set manually.

[0079] S43, obtaining an optimization adjustment plan for the rainfall station network, and when the optimization adjustment plan period exceeds the average period, the historical second weight is corrected and increased, otherwise the historical second weight is reduced;

[0080] In this embodiment, the longer the adjustment period is, the higher the requirement on the time-varying stability of the site is, and the corresponding weight should be increased.

[0081] S44, obtain the construction budget of the rainfall station. When the construction budget exceeds the historical average cost, the historical third weight is corrected and reduced, otherwise the historical third weight is increased;

[0082] S45. Normalize and correct the historical first weight, the historical second weight, and the historical third weight to calculate the current first weight, the current second weight, and the current third weight.

[0083] S5. Calculate the comprehensive score of the target site. The specific formula is as follows:

[0084] scorec=w1.scorein+w2.scoret+w3.scored

[0085] S6. Optimize the rainfall station network. The specific steps are as follows:

[0086] S61, using the spatial coordinate distribution of the current rainfall station in the rainfall station network as the initialization population;

[0087] S62, calculating the comprehensive score of the initialized population;

[0088] S63, select, cross and mutate the population to generate several new spatial distributions of rainfall stations;

[0089] S64, calculating the comprehensive score sum of the spatial distribution of new rainfall stations to form a progeny population;

[0090] S65, merging the parent and offspring populations to select the next generation input population;

[0091] S66, repeat the selection, crossover, mutation, calculation and screening operations until the convergence condition is met;

[0092] S67. The spatial distribution of several new rain gauges at the time of final convergence is taken as the optimization result of the rain gauge network.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the spatial distribution of rainfall stations, characterized in that: The specific method is as follows: Obtain the current rainfall data measured at the target site and several adjacent sites, and calculate the current information score of the target site using the information entropy of the target site; Retrieve the historical information volume score of the target site, and calculate the time-varying score of the target site according to the current information volume score of the target site and the change in the existing information volume score; Obtain estimated rainfall data calculated by remote sensing satellites, circle adjacent spaces whose difference from the estimated rainfall data of the target site is within a first preset value range, and calculate the coverage area score of the target site according to the area of ​​the adjacent spaces and the position distribution of the target site in the adjacent spaces; The current information score, time-varying score, and coverage area score are weighted to calculate the comprehensive score of the target site.

2. The method for evaluating the spatial distribution of rainfall stations according to claim 1, characterized in that: When the comprehensive scores of several rainfall stations in the region are lower than the warning value, regional optimization of the rainfall station network is enabled. The specific method is as follows: Taking the comprehensive score as the optimization goal, the optimization algorithm is used to adjust the position of each rain gauge in the rain gauge network so that the sum of the comprehensive scores of each rain gauge in the rain gauge network is maximized.

3. The method for evaluating the spatial distribution of rainfall stations according to claim 2, characterized in that: The optimization algorithm is a genetic algorithm, and the specific method is as follows: The spatial coordinate distribution of the current rainfall stations in the rainfall station network is used as the initialization population; Calculate the comprehensive score and of the initialized population; The population is selected, crossed, and mutated to generate several new spatial distributions of rainfall stations; Calculate the comprehensive score sum of the spatial distribution of new rainfall stations to form the offspring population; Merge the parent and offspring populations to select the next generation input population; Repeat the selection, crossover, mutation, calculation and screening operations until the convergence condition is met; The spatial distribution of several new rain gauges at the final convergence is taken as the optimization result of the rain gauge network.

4. The method for evaluating the spatial distribution of rainfall stations according to claim 1, characterized in that: Calculate the current information volume score of the target site. The specific method is as follows: Preset a number of data intervals and assign interval numbers to each interval; The current rainfall data measured at the target station and several adjacent stations are divided into corresponding intervals, and an array is constructed with interval numbers; Calculate the information entropy of the rainfall interval of the target station; 1 minus the absolute value of the difference between the information entropy of the target station rainfall interval and the preset standard information entropy divided by the quotient of the preset standard information entropy, and then multiplied by 100 to calculate the current information score.

5. The method for evaluating the spatial distribution of rainfall stations according to claim 4, characterized in that: Calculate the time-varying score of the target site. The specific method is as follows: Obtain the information score of several consecutive rainfalls at the target site; Calculate the standard deviation of the array composed of several rainfall information scores; 1 minus the standard deviation divided by the preset maximum standard deviation, then multiplied by 100, is used to calculate the time-varying score of the current target site.

6. The method for evaluating the spatial distribution of rainfall stations according to claim 5, characterized in that: Calculate the coverage area score of the target site as follows: Estimate the area of ​​adjacent space based on estimated rainfall data calculated by remote sensing satellites; The number of target sites and several adjacent sites is divided by the area of ​​the adjacent space to calculate the number of sites per unit area. The coverage area score of the target site is calculated by dividing the current number of sites per unit area by the preset maximum number of sites per unit area and multiplying the result by 100.

7. The method for evaluating the spatial distribution of rainfall stations according to claim 6, characterized in that: The weighted current information score, time-varying score and coverage score are as follows: The product of the current first weight and the current information score, plus the product of the current second weight and the time-varying score, plus the product of the current third weight and the coverage area score.

8. The method for evaluating the spatial distribution of rainfall stations according to claim 7, characterized in that: The calculation method of the current first weight, the current second weight and the current third weight is as follows: Initialize the first weight, the second weight, and the third weight; Calculate the difference between the information entropy score of the target site and the average information entropy score of the adjacent sites. When the absolute value of the difference increases, reduce the historical first weight, otherwise increase the historical first weight. Obtain the optimization adjustment plan of the rainfall station network. When the optimization adjustment plan cycle exceeds the average cycle, the historical second weight is increased, otherwise the historical second weight is reduced; Obtain the construction budget of the rain gauge station. When the construction budget exceeds the historical average cost, the historical third weight will be reduced, otherwise it will be increased. The normalized and corrected historical first weight, historical second weight and historical third weight are used to calculate the current first weight, current second weight and current third weight.

9. A storage medium, characterized in that: A plurality of instructions are stored, and a processor loads the instructions to execute the spatial distribution assessment method of rainfall stations as described in any one of claims 1 to 8.

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